Interactions between deleterious mutations have been insufficiently studied, despite the fact that their strength and direction are critical for understanding the evolution of genetic recombination and the buildup of mutational load in populations. We compiled a list of 758 yeast gene deletions causing growth defects (from the Munich Information Center for Protein Sequences database and ref. 7). Using BY4741 and BY4742 single-deletion strains, we carried out 639 random crosses and assayed growth curves of the resulting progeny. We show that the maximum growth rate averaged over strains lacking deletions and those with double deletions is higher than that of strains with single deletions, indicating a positive epistatic effect. This tendency is shared by genes belonging to a variety of functional classes. Based on our data and former theoretical work, we suggest that epistasis is likely to diminish the negative effects of mutations when the ability to produce biomass at high rates contributes significantly to fitness.

How do the fitness effects of several mutations combine? Despite its simplicity, this question is central to the understanding of multilocus evolution. Epistasis (the interaction between alleles at different loci), especially epistasis for fitness traits such as reproduction and survival, influences evolutionary predictions "almost whenever multilocus genetics matters". Yet very few models have sought to predict epistasis, and none has been empirically tested. Here we show that the distribution of epistasis can be predicted from the distribution of single mutation effects, based on a simple fitness landscape model. We show that this prediction closely matches the empirical measures of epistasis that have been obtained for Escherichia coli and the RNA virus vesicular stomatitis virus. Our results suggest that a simple fitness landscape model may be sufficient to quantitatively capture the complex nature of gene interactions. This model may offer a simple and widely applicable alternative to complex metabolic network models, in particular for making evolutionary predictions.

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About Me

Edward Rose Professor of Informatics,
Director of the Institute for Biomedical Informatics, Director of the Division of Informatics in the Department of Biostatistics and Epidemiology,
Senior Associate Dean for Informatics,
The Perelman School of Medicine,
University of Pennsylvania